Overview
Canvas is a collaborative workspace where you and members of the same Team can work on data together. Data analyses such as data transformation, visualization, and data mart building can be performed simply and clearly on the whiteboard-like UI, meaning even the non-technical members of your organization can get involved to bring their insights.
Basic usage of Canvas
Once you have created a Canvas, we recommend that you first change the Canvas name from the menu at the top (it will be untitled when first created).
At the bottom of Canvas, there are four icons:
- Source: use an existing Source as a Source cell.
- Code: place Python/SQL cells. Data from external sources can be processed before importing.
- Text: text box.
- Note: a sticky note, used as reference or comments to other users.
First, select the Source icon and place a Source cell in the Canvas. There is a ‘+’ button at the bottom of the cell, from which other cells can be generated/connected to perform data conversions, data mart construction, and visualizations.
Cell types are as follows. Since there are restrictions on the cells that can be connected, these are also listed.
<Type of cell>
- Source: PostgreSQL table
- Python: Cells for data conversion, allowing execution of Python code.
- SQL: Cell for data conversion, allowing execution of SQL code.
- Read only table: A PostgreSQL View
- API: Cell for generating API
- AI Chart: Cell for GUI / Prompt visualization
<Cell Relationships>
- This indicates which cells can be created from each cell.
source | Python | SQL | read only table | API | AI chart | |
source | ◯ | ◯ | ◯ | ◯ | ||
Python | ◯ | ◯ | ||||
SQL | ◯ | ◯
The parent SQL cell is automatically converted to a read-only table | Convertible from SQL cell | |||
read only table | ◯ | ◯ | ◯ | |||
API | ||||||
AI chart |
What you can do with Canvas
Data conversion
From the bottom of the cell click the ‘+’ button. From here you can choose Transform, SQL, or Python.
After placing the desired cell in the Canvas, click on the cell and press the ‘>’ button to switch to the Editor screen.
The structure of the Editor screen is as follows:
SQL cells
Write SQL and click Run in the upper right corner to execute the SQL.
Python cells
Click ‘Run’ to execute the test function locally, or ‘Run on cloud’ to execute the main process in-browser.
Conversion to Source
From the ‘+’ button at the bottom of a Python/SQL cell which has output the results of the operation, select ‘Source.’ Set a name, save, and the execution result will be placed as a Source table.
<SQL cell>
Select Turn into Read-only Table from Developer Options at the top of the SQL cell where the execution results were output. Then, in the converted Read-only Table (same cell), select source from the + button at the bottom, give it a name, and press Create source to create the source.
Data visualization
coming soon
API Creation
The ‘+’ button at the bottom of the cell allows you to create four types of APIs: Query, Create, Delete, and Update. These will be discussed in the API section.